A Multi-Task Approach for Real-Time Quality of Experience Factors Prediction from Physiological Data

In the multimedia field, the quality of experience (QoE) is rightfully seen as the center metric in research, around which each piece of the network is designed, especially for next-generation networks. Even if an increasing number of models using various data as inputs are now available, some major problems remain, such as the implementation of quality of experience measurement for real-time applications. The 'Human Factors' are the primary reason for the impossibility to correctly predict the QoE for real-time applications since these factors can't be measured easily and swiftly. For this reason, we present in this paper a Multi-Task model to predict multiple QoE influence factors at once from physiological data to save time in the training process and during the prediction. To test the model, we use a publicly available dataset named SoPMD, which contains recordings from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiratory signals obtained during a quality assessment experiment, where QoE factors are gathered as labels. Our Multi-Task model has been tested using different features extracted from EEG and presents results up to 68.51% in accuracy. This model could be used in a real-time regulation loop to predict QoE factors faster than single-task models, for an enhanced QoE prediction, as this model can predict the five factors at the same time.